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The Gender Gap Tracker: Using Natural Language Processing to measure gender bias in media
Fatemeh Torabi Asr1, Mohammad Mazraeh1, Alexandre Lopes2
1Discourse Processing Lab, Department of Linguistics, Simon Fraser University, Burnaby, Canada.
Plos One
|January 29, 2021
Summary
Men are quoted three times more often than women in Canadian news, according to the Gender Gap Tracker. This study highlights media gender bias and aims to encourage diverse sourcing in reporting.
Area of Science:
- Media Studies
- Computational Linguistics
- Gender Studies
Background:
- Gender bias in media representation remains a significant concern.
- Understanding the extent of this bias requires robust analytical tools.
Purpose of the Study:
- To quantify gender disparities in news sources using a novel software system.
- To analyze the representation of men and women quoted in Canadian news media.
Main Methods:
- Development of the Gender Gap Tracker, a software system for analyzing news text.
- Utilizing Natural Language Processing (NLP) technology and curated linguistic resources.
- Automated analysis of online publications from seven English-language Canadian news outlets (Oct 2018 - Sep 2020).
Main Results:
- Men were quoted approximately three times more frequently than women across the analyzed news outlets.
- Consistent gender quoting patterns were observed, though variations existed across outlets and time.
- Analysis provided insights into quoted proportions by gender, news organization, and author gender.
Conclusions:
- The study reveals a persistent gender gap in news sourcing, with men being disproportionately represented.
- The Gender Gap Tracker serves as a tool to measure progress and motivate news organizations towards more diverse sourcing.
- Encouraging diverse voices in reporting is crucial for equitable media representation.
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